Text Classification
Transformers
TensorFlow
English
distilbert
generated_from_keras_callback
twitter
text-embeddings-inference
Instructions to use ZachBeesley/Tweet-Emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZachBeesley/Tweet-Emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ZachBeesley/Tweet-Emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ZachBeesley/Tweet-Emotion") model = AutoModelForSequenceClassification.from_pretrained("ZachBeesley/Tweet-Emotion") - Notebooks
- Google Colab
- Kaggle
Commit ·
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README.md
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- tweet_eval
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language:
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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- tweet_eval
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language:
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- en
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widget:
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- text: "Is this review positive or negative? Review: Best cast iron skillet you will ever buy."
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example_title: "Sentiment analysis"
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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